LLMs are AI models trained on vast amounts of text data to generate human-like text, but they struggle with deeper understanding and interpretation of context, resulting in occasional errors or nonsensical output. NLU systems, on the other hand, focus on enabling machines to comprehend and interpret human language, analyzing text to extract meaning, context, sentiment, and intent. LLMs are not suitable for tasks requiring precise understanding of language, such as intent recognition and contextual interpretation, which are strengths of NLU systems. Additionally, NLUs are more focused, efficient, customizable, and robust than LLMs, making them better suited for real-time applications and resource-constrained environments. The two technologies are complementary and can be combined to develop more advanced AI applications that better understand and serve human needs.